Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ZJU-REAL/Easelnpx agentmods add skills/zju-real/easel/skill-wechat-publisherWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zju-real/easel/skill-wechat-publisher)<a href="https://agentmods.dev/skills/zju-real/easel/skill-wechat-publisher"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-wechat-publisher/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zju-real/easel/skill-wechat-publisher"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-wechat-publisher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.02694 |
| Opus 5 | $0.00060 | $0.01347 |
| Sonnet 5 | $0.00024 | $0.00539 |
| Haiku 4.5 | $0.00012 | $0.00269 |
Grade A, and why
skill-wechat-publisher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
微信公众号文章自动创作与发布
从素材输入到公众号草稿箱的完整自动化流程：用户只需提供话题或参考资料，skill 完成搜索调研、撰写、配图、排版、AI 味自检、发布。
不要调用
baoyu-post-to-wechatskill。 路由时同时看到两者，一律选本 skill（wechat-publisher），它带多账号 / 主题排版 / 反 AI 检测 gate。
⚠️ 环境依赖（部分可跑）
| 能力 | 状态 | 依赖 |
|---|---|---|
公众号发草稿（publish.py → 官方 HTTP API） |
✅ 可跑 | wechat-publisher.yaml 里的 app_id / app_secret + IP 白名单 |
反 AI 检测（ai_score.py） |
✅ 可跑 | 纯本地，无外部依赖 |
MD→公众号排版（html_converter.py） |
✅ 可跑 | 纯本地 |
生成配图（generate_image.py） |
❌ 当前不可用 | 需图像 API key（OpenAI Images / Gemini 代理） |
多平台同步（multi_publish.py，阶段七） |
❌ 当前不可用 | 需浏览器 + Wechatsync Chrome 扩展 |
核心链路（写作→排版→反 AI 检测→发草稿）在配好 app_id/app_secret 后可跑；配图缺 key 时改用外部生图或跳过，多平台同步默认不启用。
账号与人格
默认 2 个账号（见 wechat-publisher.yaml）：main（刷屏AI / 飞哥 / refined-blue，热情北京口语，面向 AI 产品）与 tech（蒜是哪根葱 / 葱哥 / minimal-mono，冷幽默技术直男，面向工程实践）。不指定 --account 用 main。
必须按当前账号的 voice 改写语气 —— 两个号写出明显风格差异，这本身就是反 AI 检测的关键（平台对每个号建历史文风基线）。
前置条件
cp wechat-publisher.yaml.example wechat-publisher.yaml # 填 app_id / app_secret / author / theme
python3 skills/openclaw/skill-wechat-publisher/scripts/wechat_api.py list-accounts
# 验证 API 连接
cd skills/openclaw/skill-wechat-publisher/scripts && python3 -c "from wechat_api import get_access_token; print('OK:', get_access_token()[:10])"
pip install requests pyyaml --break-system-packages 2>/dev/null || pip install requests pyyaml
配置文件固定放 skill 根目录（config.py::_find_unified_yaml() 只查此处），账号下必须有 app_id 和 app_secret。API 参数细节见 references/api_reference.md，错误码见 references/errors.md。
完整工作流程（7 阶段，第 7 为可选 opt-in）
阶段一：理解需求与收集素材
搞清用户要什么，同时采集真人味原料（具体人名/时间/金额/产品版本/踩过的坑 —— 反 AI 检测最重要原料）。识别目标账号（AI 产品→main，工程→tech），选定 article_structure + opening_hook。产出 outputs/主题名/brief.md。
阶段二：全网信息搜索与整理
既搜权威层（WebSearch：资讯/数据/案例/官方报告），也搜真人层（Reddit/HN/V2EX/即刻/X/小红书的原话作语料）。关键数据多源交叉。产出 research.md，每条标来源、分层。
阶段三：撰写骨架稿（第一轮）
按结构写初稿，允许有 AI 味（下一阶段专门人味化）。article_structure 8 种、opening_hook 7 种的完整选择规则与写法见 references/article-structures.md。骨架稿阶段就主动混用行内标色（见 references/inline-markup.md）。避免"首先/其次""值得一提的是""随着…的发展"等 AI 套话。
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/image-styles/blue.json 3.0 KB
- assets/image-styles/data-chart.json 2.4 KB
- assets/image-styles/flat-editorial.json 4.9 KB
- assets/image-styles/hand-drawn-blue.json 2.3 KB
- assets/image-styles/illustrated-warm.json 3.3 KB
- assets/image-styles/infographic-blue.json 5.2 KB
- assets/image-styles/infographic-dark.json 5.4 KB
- assets/image-styles/infographic-mint.json 5.2 KB
- assets/image-styles/infographic-warm.json 5.4 KB
- assets/image-styles/knowledge-card.json 2.3 KB
- assets/image-styles/magazine-editorial.json 2.3 KB
- assets/image-styles/marker-coral.json 6.4 KB
- assets/image-styles/marker-lime.json 6.6 KB
- assets/image-styles/marker-pink.json 6.4 KB
- assets/image-styles/marker-sky.json 6.4 KB
- assets/image-styles/marker-violet.json 6.4 KB
- assets/image-styles/meme-illustration.json 2.3 KB
- assets/image-styles/mint.json 3.1 KB
- assets/image-styles/morandi.json 3.4 KB
- assets/image-styles/previews/blue.webp 104 KB
- assets/image-styles/previews/data-chart.webp 16 KB
- assets/image-styles/previews/flat-editorial.webp 110 KB
- assets/image-styles/previews/hand-drawn-blue.webp 18 KB
- assets/image-styles/previews/illustrated-warm.webp 31 KB
- assets/image-styles/previews/infographic-blue.webp 160 KB
- assets/image-styles/previews/infographic-dark.webp 142 KB
- assets/image-styles/previews/infographic-mint.webp 150 KB
- assets/image-styles/previews/infographic-warm.webp 163 KB
- assets/image-styles/previews/knowledge-card.webp 22 KB
- assets/image-styles/previews/magazine-editorial.webp 14 KB
- assets/image-styles/previews/marker-coral.webp 41 KB
- assets/image-styles/previews/marker-lime-template.webp 34 KB
- assets/image-styles/previews/marker-lime.webp 39 KB
- assets/image-styles/previews/marker-pink.webp 38 KB
- assets/image-styles/previews/marker-sky.webp 48 KB
- assets/image-styles/previews/marker-violet.webp 43 KB
- assets/image-styles/previews/meme-illustration.webp 36 KB
- assets/image-styles/previews/mint.webp 96 KB
- assets/image-styles/previews/morandi.webp 82 KB
- assets/image-styles/previews/slice-of-life.webp 46 KB
- assets/image-styles/previews/warm-handdrawn.webp 79 KB
- assets/image-styles/previews/xiaohongshu-colorful.webp 24 KB
- assets/image-styles/README.md 29 KB
- assets/image-styles/slice-of-life.json 4.9 KB
- assets/image-styles/warm-handdrawn.json 4.4 KB
- assets/image-styles/xiaohongshu-colorful.json 2.3 KB
- assets/theme-previews/academic-paper.html 13 KB
- assets/theme-previews/business-navy.html 14 KB
- assets/theme-previews/elegant-ink.html 13 KB
- assets/theme-previews/girly-pink.html 13 KB
- assets/theme-previews/index.html 27 KB
- assets/theme-previews/ink-wash.html 14 KB
- assets/theme-previews/magazine-grid.html 13 KB
- assets/theme-previews/minimal-bw.html 14 KB
- assets/theme-previews/minimal-mono.html 13 KB
- assets/theme-previews/mint-fresh.html 14 KB
- assets/theme-previews/news-bold.html 13 KB
- assets/theme-previews/README.md 1.7 KB
- assets/theme-previews/refined-blue.html 15 KB
- assets/theme-previews/sage-premium.html 14 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 118 lines · 119 tokens per session scan A 0b02d53bb44d
skill-wechat-publisher is a skill published in the GitHub repository ZJU-REAL/Easel (794 stars, last pushed yesterday), licensed Apache-2.0. It adds 119 tokens to every session and 2,694 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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